Do the approaches below have any effect on evaluation:

(1) Evaluating each model translation separately or
(2) in the same data list?

For example, when translating the English word "Hello" to Afrikaans:

(1) Evaluating each model translation separately

data1 = [
{
"src": "hello",
"mt": "hallllo"
},
]
model_output1 = model.predict(data1, batch_size=8, gpus=1)
print (model_output1)

data2 = [
{
"src": "hello",
"mt": "hallo"
}
]
model_output2 = model.predict(data2, batch_size=8, gpus=1)
print (model_output2)

(2) in the same data list

data = [
{
"src": "hello",
"mt": "hallo"
},
{
"src": "hello",
"mt": "hallllo"
},
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)